The Methodology
The AI-Assisted
Workflow
Eight steps. From the first conversation with AI to a deployed, documented application. This workflow is the spine of Module 3 and every project that follows.
A single slide showing all eight steps of the AI-assisted workflow — from Brainstorm and Specify through Implement, Test, and Iterate, to Document, Deploy, and Maintain. This overview runs as the reference map throughout Module 3, appearing at the start of each lecture to orient you within the full arc.
Brainstorm
Module 3, L3.1Define the problem. Explore the solution space with AI as a thinking partner.
Generates options, challenges assumptions, surfaces edge cases you hadn't considered.
Specify
Module 3, L3.2Write a one-paragraph specification covering task, context, constraints, and output format. Run `git init` before you open the AI tool.
Reviews and improves your specification. Identifies ambiguities before they become rework.
Implement
Module 3, L3.3Direct the AI at the right level (3, 4, or 5). Review each output before moving forward.
Writes code from your specification — functions, files, or multi-file systems depending on the level.
Test
Module 3, L3.4Commit before each agent run. Run tests on the output. Validate against your original specification.
Generates test cases, writes test code, and at Level 5 runs tests and reports results.
Iterate
Module 3, L3.5Identify what doesn't meet the specification. Reframe the problem, not just the symptom.
Refactors code on request, diagnoses failures, suggests architectural improvements.
Document
Module 3, L3.6Write the README and decision log. Record why, not just what — future-you will be grateful.
Generates docstrings, API docs, and README scaffolding from your codebase.
Deploy
Module 3, L3.7Choose the right deployment target for the audience. Makers deploy differently than enterprises.
Writes deployment configs, Dockerfiles, systemd units, and step-by-step deployment guides.
Maintain
Module 5, L5.6Monitor, update dependencies, and evolve the application as requirements change.
Diagnoses bugs from logs, suggests dependency upgrades, writes migration scripts.
The Core Skill
Four elements of a well-framed prompt
Framing is disciplined communication: giving the AI the information it needs to act correctly. Not all four elements are required for every prompt — but for anything at Level 4, all four should be present.
What should happen? Be specific about the output type.
What does the AI need to know to get it right? Your domain knowledge goes here.
What must not happen? Narrow the solution space explicitly.
How should the result be structured? Be explicit about what you want back.
Framing is the core skill of AI-assisted coding. This lecture introduces the four-element structure — task, context, constraints, and output format — that turns a vague request into a specification the AI can act on. A well-framed prompt reduces correction cycles; a poorly framed one produces plausible-looking output built on wrong assumptions.
The specification habit
Before you open the AI tool for any task larger than a snippet, write a one-paragraph specification. It doesn't need to be formal. It needs to cover task, context, constraints, and output format. That paragraph is your prompt.
This is a habit, not a technique. The first few times it feels like extra work. After a few sessions it becomes automatic — and the quality and consistency of your outputs will reflect that.